Ideally, a set of seismic imaging data may be represented by a unique subsurface geological model. However, typically subsurface geological models derived from seismic imaging are not unique; that is, many combinations of subsurface geological model representations and model parameter values may be used to satisfy the same imaging conditions. Typically, this variation is due to limitations in acquiring the seismic data, such as, for example, small offset distances between sources and receivers and the limited range of azimuth coverage. Other factors contributing to the non-uniqueness of subsurface geological models are, for example: finite frequency band, noise, shadow zones, and limitations in the ray method and theory of wave propagation used (e.g., in which some factors are not accounted for, such as, anisotropy (directional dependency) or isotropy (directional independence), dispersion from attenuation, scattering, high frequency assumptions, etc.). Thus, deriving an accurate model is an interpretative process based on information obtained from seismic imaging, wells, and a priori geological knowledge. This process is typically very demanding, normally requiring massive computational power and intensive human involvement.
The resulting derived subsurface model normally consists of several million data points, each containing information about structure geometry and medium properties. Therefore, altering the model is typically a very complicated and laborious task.
A need exists for an accurate and efficient means for altering a subsurface geological model while minimizing computational barriers and the need for user input.